NDLI: Improving EEG-BCI analysis for low certainty subjects by using dictionary learning

Content Provider	IEEE Xplore Digital Library
Author	Victorino, J. Noirhomme, Q. Lule, D. Kleih, S.C. Chatelle, C. Halder, S. Demertzi, A. Bruno, M.-A. Gosseries, O. Vanhaudenhuyse, A. Schnakers, C. Thonnard, M. Soddu, A. Kubler, A. Laureys, S. Gomez, F.
Copyright Year	2015
Description	Author affiliation: Inst. of Med. Psychol. & Behavioural Neurobiol., Univ. of Tubingen, Tubingen, Germany (Kleih, S.C.; Halder, S.; Kubler, A.) \|\| Coma Sci. Group, Univ. Hosp. of Liege, Liege, Belgium (Noirhomme, Q.; Lule, D.; Chatelle, C.; Demertzi, A.; Bruno, M.-A.; Gosseries, O.; Vanhaudenhuyse, A.; Schnakers, C.; Thonnard, M.; Laureys, S.) \|\| Univ. Central, Bogota, Colombia (Victorino, J.; Gomez, F.) \|\| Phys. & Astron. Dept., Western Univ., London, ON, Canada (Soddu, A.)
Abstract	The diagnosis of patients with Disorders Of Consciousness represents a challenge in the clinical routine. Recently, Brain Computer Interfaces based in Electroencephalography (EEG-BCI) have been used to detect signs of consciousness in these patients. This approach allows to discover brain responses to command following. Nevertheless, a reliable BCI strategy must to be able to determine the commands with high levels of certainty. Current results reported in the literature evidence that about 25% of the subjects in which BCI is used may have low performances, near to the chance level, even in collaborating subjects. In this work, we propose a novel approach based on dictionary learning representations aimed to improve performance in low certainty subjects. We propose to introduce an intermediate representation scheme, based on sparse dictionaries, before feature selection step. Our main assumption is that by using these representations we can capture more efficiently the EEG signal structure for subjects responses. The results show that using the new representation the weighted average performance in command following outcome the previews proposed methods of 64.8% to 67.9%. Higher improvements in performance were obtained for low certainly subjects. Our results suggests that this approach may improve BCI-EEG performance in low certainly subjects.
Starting Page	1
Ending Page	7
File Size	455330
Page Count	7
File Format	PDF
e-ISBN	9781467394611
DOI	10.1109/STSIVA.2015.7330408
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-09-02
Publisher Place	Colombia
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Training Electrodes Visualization Dictionaries Electroencephalography Yttrium Testing
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
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